KDD 1999 Cup Project
Data Preprocessing
Libraries
# Others
library(caret)
library(doParallel)
library(dplyr)
library(forcats)
library(ggplot2)
library(gmodels)
library(pROC)
# ANN
library(data.table)
library(keras)
library(mltools)
library(tfruns)Other Options
options(scipen=10000)
install_keras(method="conda", envname="r", tensorflow="gpu")Collecting package metadata (current_repodata.json): ...working... done
Solving environment: ...working... done
==> WARNING: A newer version of conda exists. <==
current version: 4.9.2
latest version: 4.10.1
Please update conda by running
$ conda update -n base -c defaults conda
# All requested packages already installed.
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Installation complete.Data Load
kdd_header = as.character(unlist(kdd_header[1,]))
Error in kdd_header[1, ] : incorrect number of dimensionsData Cleaning
Convert discrete variables
kdd$land = as.factor(kdd$land)
levels(kdd$land) = list("other"=0, "same"=1)
kdd$logged_in = as.factor(kdd$logged_in)
levels(kdd$logged_in) = list("fail"=0, "success"=1)
kdd$root_shell = as.factor(kdd$root_shell)
levels(kdd$root_shell) = list("nonroot"=0, "root"=1)
kdd$su_attempted = as.factor(kdd$su_attempted)
levels(kdd$su_attempted) = list("no"=0, "yes"=1)
kdd$is_hot_login = as.factor(kdd$is_hot_login)
levels(kdd$is_hot_login) = list("no"=0, "yes"=1)
kdd$is_guest_login = as.factor(kdd$is_guest_login)
levels(kdd$is_guest_login) = list("no"=0, "yes"=1)Clean target variable
levels(kdd$eventType) = gsub("\\.", "", levels(kdd$eventType))Drop column with no variance
kdd$num_outbound_cmds = NULLDrop nulls - there shouldn’t be any
colSums(is.na(kdd)) duration protocol_type service flag src_bytes
0 0 0 0 0
dst_bytes land wrong_fragment urgent hot
0 0 0 0 0
num_failed_logins logged_in num_compromised root_shell su_attempted
0 0 0 0 6
num_root num_file_creations num_shells num_access_files is_hot_login
0 0 0 0 0
is_guest_login count srv_count serror_rate srv_serror_rate
0 0 0 0 0
rerror_rate srv_rerror_rate same_srv_rate diff_srv_rate srv_diff_host_rate
0 0 0 0 0
dst_host_count dst_host_srv_count dst_host_same_srv_rate dst_host_diff_srv_rate dst_host_same_src_port_rate
0 0 0 0 0
dst_host_srv_diff_host_rate dst_host_serror_rate dst_host_srv_serror_rate dst_host_rerror_rate dst_host_srv_rerror_rate
0 0 0 0 0
eventType
0 kdd = kdd[complete.cases(kdd),]
colSums(is.na(kdd)) duration protocol_type service flag src_bytes
0 0 0 0 0
dst_bytes land wrong_fragment urgent hot
0 0 0 0 0
num_failed_logins logged_in num_compromised root_shell su_attempted
0 0 0 0 0
num_root num_file_creations num_shells num_access_files is_hot_login
0 0 0 0 0
is_guest_login count srv_count serror_rate srv_serror_rate
0 0 0 0 0
rerror_rate srv_rerror_rate same_srv_rate diff_srv_rate srv_diff_host_rate
0 0 0 0 0
dst_host_count dst_host_srv_count dst_host_same_srv_rate dst_host_diff_srv_rate dst_host_same_src_port_rate
0 0 0 0 0
dst_host_srv_diff_host_rate dst_host_serror_rate dst_host_srv_serror_rate dst_host_rerror_rate dst_host_srv_rerror_rate
0 0 0 0 0
eventType
0 Add new target variable
kdd = kdd %>% mutate(eventCategory = case_when(eventType %in% c("back","land","neptune",
"pod","smurf","teardrop") ~ "dos",
eventType %in% c("buffer_overflow","loadmodule",
"perl","rootkit") ~ "u2r",
eventType %in% c("ftp_write","guess_passwd","imap",
"multihop","phf","spy","warezclient",
"warezmaster") ~ "r21",
eventType %in% c("ipsweep","nmap","portsweep",
"satan") ~ "probe",
TRUE ~ "normal"))
kdd$eventCategory = as.factor(kdd$eventCategory)Data Exploration and Additional Processing
Correlation Matrix
get_upper_matrix <- function(matrix){
matrix[lower.tri(matrix)] <- NA
return(matrix)
}
corr_mat = melt(get_upper_matrix(cor(kdd[sapply(kdd,is.numeric)])))The melt generic in data.table has been passed a matrix and will attempt to redirect to the relevant reshape2 method; please note that reshape2 is deprecated, and this redirection is now deprecated as well. To continue using melt methods from reshape2 while both libraries are attached, e.g. melt.list, you can prepend the namespace like reshape2::melt(get_upper_matrix(cor(kdd[sapply(kdd, is.numeric)]))). In the next version, this warning will become an error.ggplot(data = corr_mat, aes(x=Var1, y=Var2, fill=value)) +
geom_tile(color="white") +
scale_fill_gradient2(midpoint=0,limit=c(-1,1)) +
theme(axis.text.x=element_text(angle=90, vjust=.5, hjust=1))
Correlation measures
corr_filt = corr_mat[abs(corr_mat$value) > .9 &
abs(corr_mat$value) < 1 &
!is.na(corr_mat$value),]
corr_filt[order(corr_filt$Var1),]Drop columns for high correlation
kdd$num_compromised = NULL
kdd$serror_rate = NULL
kdd$rerror_rate = NULL
kdd$srv_serror_rate = NULL
kdd$srv_rerror_rate = NULL
kdd$srv_count = NULL
kdd$dst_host_srv_rerror_rate = NULL
kdd$dst_host_same_srv_rate = NULL
kdd$dst_host_srv_serror_rate = NULL
kdd$is_hot_login = NULLRecheck correlation matrix
corr_mat = melt(get_upper_matrix(cor(kdd[sapply(kdd,is.numeric)])))The melt generic in data.table has been passed a matrix and will attempt to redirect to the relevant reshape2 method; please note that reshape2 is deprecated, and this redirection is now deprecated as well. To continue using melt methods from reshape2 while both libraries are attached, e.g. melt.list, you can prepend the namespace like reshape2::melt(get_upper_matrix(cor(kdd[sapply(kdd, is.numeric)]))). In the next version, this warning will become an error.ggplot(data = corr_mat, aes(x=Var1, y=Var2, fill=value)) +
geom_tile(color="white") +
scale_fill_gradient2(midpoint=0,limit=c(-1,1)) +
theme(axis.text.x=element_text(angle=90, vjust=.5, hjust=1))
Drop additional columns
# Do not use due to more severe class imbalance
kdd$eventType = NULL
# Do not use due to high levels
kdd$service = NULLCategoric Variables
## protocol_type
as.data.frame(sort(table(kdd$protocol_type)))ggplot(kdd, mapping=aes(x=fct_rev(fct_infreq(protocol_type)))) +
geom_bar(stat="count", fill="steelblue") +
xlab("protocol type") +
ylab("count of protocol types") +
scale_y_continuous(n.breaks=10) +
theme_minimal() +
coord_flip()
### flag
as.data.frame(sort(table(kdd$flag)))ggplot(kdd, mapping=aes(x=fct_rev(fct_infreq(flag)))) +
geom_bar(stat="count", fill="steelblue") +
xlab("flag type") +
ylab("count of flags") +
scale_y_continuous(n.breaks=10) +
theme_minimal() +
coord_flip()
## eventCategory
as.data.frame(sort(table(kdd$eventCategory)))ggplot(kdd, mapping=aes(x=fct_rev(fct_infreq(eventCategory)))) +
geom_bar(stat="count", fill="steelblue") +
xlab("event type") +
ylab("count of event types") +
scale_y_continuous(n.breaks=10) +
theme_minimal() +
coord_flip()
## land
as.data.frame(sort(table(kdd$land)))ggplot(kdd, mapping=aes(x=fct_rev(fct_infreq(land)))) +
geom_bar(stat="count", fill="steelblue") +
xlab("land type") +
ylab("count of land type") +
scale_y_continuous(n.breaks=10) +
theme_minimal() +
coord_flip()
## logged in
as.data.frame(sort(table(kdd$logged_in)))ggplot(kdd, mapping=aes(x=fct_rev(fct_infreq(logged_in)))) +
geom_bar(stat="count", fill="steelblue") +
xlab("logged in status") +
ylab("count of logged in status") +
scale_y_continuous(n.breaks=10) +
theme_minimal() +
coord_flip()
## root shell
as.data.frame(sort(table(kdd$root_shell)))ggplot(kdd, mapping=aes(x=fct_rev(fct_infreq(root_shell)))) +
geom_bar(stat="count", fill="steelblue") +
xlab("shell level") +
ylab("count of shell level") +
scale_y_continuous(n.breaks=10) +
theme_minimal() +
coord_flip()
## su attempt
as.data.frame(sort(table(kdd$su_attempted)))ggplot(kdd, mapping=aes(x=fct_rev(fct_infreq(su_attempted)))) +
geom_bar(stat="count", fill="steelblue") +
xlab("su attempt") +
ylab("count of su attempt") +
scale_y_continuous(n.breaks=10) +
theme_minimal() +
coord_flip()
## guest login
as.data.frame(sort(table(kdd$is_guest_login)))ggplot(kdd, mapping=aes(x=fct_rev(fct_infreq(is_guest_login)))) +
geom_bar(stat="count", fill="steelblue") +
xlab("guest login") +
ylab("count of guest login") +
scale_y_continuous(n.breaks=10) +
theme_minimal() +
coord_flip()
Numeric Variables
colfunc = colorRampPalette(c("orange2","orangered4"))
boxplot(duration~eventCategory, data=kdd,
col=colfunc(length(levels(kdd$eventCategory))),
outline=FALSE)
boxplot(src_bytes~eventCategory, data=kdd,
col=colfunc(length(levels(kdd$eventCategory))),
outline=FALSE)
boxplot(dst_bytes~eventCategory, data=kdd,
col=colfunc(length(levels(kdd$eventCategory))),
outline=FALSE)
boxplot(count~eventCategory, data=kdd,
col=colfunc(length(levels(kdd$eventCategory))),
outline=FALSE)
boxplot(dst_host_count~eventCategory, data=kdd,
col=colfunc(length(levels(kdd$eventCategory))),
outline=FALSE)
boxplot(dst_host_srv_count~eventCategory, data=kdd,
col=colfunc(length(levels(kdd$eventCategory))),
outline=FALSE)
boxplot(hot~eventCategory, data=kdd,
col=colfunc(length(levels(kdd$eventCategory))),
outline=FALSE)
boxplot(num_file_creations~eventCategory, data=kdd,
col=colfunc(length(levels(kdd$eventCategory))),
outline=FALSE)
boxplot(same_srv_rate~eventCategory, data=kdd,
col=colfunc(length(levels(kdd$eventCategory))),
outline=FALSE)
boxplot(diff_srv_rate~eventCategory, data=kdd,
col=colfunc(length(levels(kdd$eventCategory))),
outline=FALSE)
boxplot(dst_host_diff_srv_rate~eventCategory, data=kdd,
col=colfunc(length(levels(kdd$eventCategory))),
outline=FALSE)
boxplot(dst_host_same_src_port_rate~eventCategory, data=kdd,
col=colfunc(length(levels(kdd$eventCategory))),
outline=FALSE)
boxplot(dst_host_srv_diff_host_rate~eventCategory, data=kdd,
col=colfunc(length(levels(kdd$eventCategory))),
outline=FALSE)
boxplot(dst_host_serror_rate~eventCategory, data=kdd,
col=colfunc(length(levels(kdd$eventCategory))),
outline=FALSE)
boxplot(dst_host_rerror_rate~eventCategory, data=kdd,
col=colfunc(length(levels(kdd$eventCategory))),
outline=FALSE)
Data Analysis
Training and Test data
set.seed(1)
train_index = createDataPartition(kdd$eventCategory, p=0.9, list=FALSE)
kdd_test_x = as.data.frame(kdd[-train_index,-30])
kdd_test_y = as.data.frame(kdd[-train_index, 30])
colnames(kdd_test_y)[1] = "eventCategory"
kdd_train = kdd[train_index,]
val_index = createDataPartition(kdd_train$eventCategory, p=0.1, list=FALSE)
kdd_val_x = as.data.frame(kdd_train[val_index,-30])
kdd_val_y = as.data.frame(kdd_train[val_index, 30])
colnames(kdd_val_y)[1] = "eventCategory"
kdd_train_x = kdd_train[-val_index,-30]
kdd_train_y = kdd_train[-val_index, 30]Naive Bayes
set.seed(1)
cl = makePSOCKcluster(detectCores(logical=FALSE))
registerDoParallel(cl)
kdd_nb = caret::train(kdd_train_x, kdd_train_y,
method="nb",
trControl=trainControl(method="cv",
number=8,
sampling="down",
allowParallel=TRUE,
classProbs=TRUE,
savePredictions=TRUE))Random Forest
set.seed(1)There were 50 or more warnings (use warnings() to see the first 50)cl = makePSOCKcluster(detectCores(logical=FALSE))
registerDoParallel(cl)
kdd_rf = caret::train(kdd_train_x,kdd_train_y,
method="rf",
trControl=trainControl(method="cv",
number=10,
sampling="down",
allowParallel=TRUE,
savePredictions=TRUE,
classProbs=TRUE,
verboseIter=TRUE),
ntree=15)Aggregating results
Selecting tuning parameters
Fitting mtry = 15 on full training setprobs_rf = predict(kdd_rf, kdd_test_x, type="prob")
raw_rf = predict(kdd_rf, kdd_test_x, type="raw")
stopCluster(cl)
CrossTable(raw_rf, kdd_test_y$eventCategory, prop.chisq=FALSE, prop.t=FALSE,
dnn=c("predicted","actual"))
Cell Contents
|-------------------------|
| N |
| N / Row Total |
| N / Col Total |
|-------------------------|
Total Observations in Table: 49399
| actual
predicted | dos | normal | probe | r21 | u2r | Row Total |
-------------|-----------|-----------|-----------|-----------|-----------|-----------|
dos | 38841 | 18 | 3 | 0 | 0 | 38862 |
| 0.999 | 0.000 | 0.000 | 0.000 | 0.000 | 0.787 |
| 0.992 | 0.002 | 0.007 | 0.000 | 0.000 | |
-------------|-----------|-----------|-----------|-----------|-----------|-----------|
normal | 150 | 8603 | 3 | 10 | 0 | 8766 |
| 0.017 | 0.981 | 0.000 | 0.001 | 0.000 | 0.177 |
| 0.004 | 0.884 | 0.007 | 0.089 | 0.000 | |
-------------|-----------|-----------|-----------|-----------|-----------|-----------|
probe | 58 | 227 | 400 | 1 | 0 | 686 |
| 0.085 | 0.331 | 0.583 | 0.001 | 0.000 | 0.014 |
| 0.001 | 0.023 | 0.976 | 0.009 | 0.000 | |
-------------|-----------|-----------|-----------|-----------|-----------|-----------|
r21 | 23 | 527 | 0 | 95 | 0 | 645 |
| 0.036 | 0.817 | 0.000 | 0.147 | 0.000 | 0.013 |
| 0.001 | 0.054 | 0.000 | 0.848 | 0.000 | |
-------------|-----------|-----------|-----------|-----------|-----------|-----------|
u2r | 73 | 352 | 4 | 6 | 5 | 440 |
| 0.166 | 0.800 | 0.009 | 0.014 | 0.011 | 0.009 |
| 0.002 | 0.036 | 0.010 | 0.054 | 1.000 | |
-------------|-----------|-----------|-----------|-----------|-----------|-----------|
Column Total | 39145 | 9727 | 410 | 112 | 5 | 49399 |
| 0.792 | 0.197 | 0.008 | 0.002 | 0.000 | |
-------------|-----------|-----------|-----------|-----------|-----------|-----------|
auc(multiclass.roc(as.factor(kdd_test_y$eventCategory), probs_rf))Multi-class area under the curve: 0.9922varImp(kdd_rf)rf variable importance
only 20 most important variables shown (out of 29)Artificial Neural Network
NOTE: Tensorflow run is not setup to run all parameters, it can take 12 or more hours
# One Hot Encoding
set.seed(1)
kdd_train_y_df = as.data.frame(kdd_train_y)
colnames(kdd_train_y_df)[1] = "eventCategory"
kdd_train_y_ohe = as.data.frame(one_hot(as.data.table(kdd_train_y_df)))
kdd_train_x_ohe = as.data.frame(one_hot(as.data.table(kdd_train_x)))
kdd_val_y_ohe = as.data.frame(one_hot(as.data.table(kdd_val_y)))
kdd_val_x_ohe = as.data.frame(one_hot(as.data.table(kdd_val_x)))
kdd_test_y_ohe = as.data.frame(one_hot(as.data.table(kdd_test_y)))
kdd_test_x_ohe = as.data.frame(one_hot(as.data.table(kdd_test_x)))
# Modeling
# nodes=8,32,64;lr=.1,.01,.001;batch=32,50,100;epoch=50,100;activation=relu,sigmoid,tanh
cl = makePSOCKcluster(detectCores(logical=FALSE))
registerDoParallel(cl)
runs <- tuning_run("D:/Programming/git/CSC532-Intro-To-Machine-Learning/99_final/kdd_hyper.R",
flags = list(nodes=c(32),
learning_rate=c(0.001),
batch_size=c(32),
epochs=c(100),
activation=c("relu"))
)1 total combinations of flags
(use sample parameter to run a random subset)YTraining run 1/1 (flags = list(32, 0.001, 32, 100, "relu"))
Using run directory runs/2021-05-03T18-00-20Z
> # Parameters for dataset
> FLAGS <-flags(flag_numeric("nodes", 30),
+ flag_numeric("batch_size", 100),
+ flag_string("ac ..." ... [TRUNCATED]
> # Model
> model <- keras_model_sequential() %>%
+ layer_dense(units=FLAGS$nodes, activation=FLAGS$activation, input_shape=dim(kdd_train_x_ohe)[2]) .... [TRUNCATED] 2021-05-03 14:00:25.050475: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
2021-05-03 14:00:37.555736: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-05-03 14:00:37.557294: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library nvcuda.dll
2021-05-03 14:00:37.578298: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:2b:00.0 name: GeForce GTX 1080 computeCapability: 6.1
coreClock: 1.7335GHz coreCount: 20 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 298.32GiB/s
2021-05-03 14:00:37.578454: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
2021-05-03 14:00:38.022126: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll
2021-05-03 14:00:38.022218: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll
2021-05-03 14:00:38.323078: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll
2021-05-03 14:00:38.354252: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll
2021-05-03 14:00:38.355115: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cusolver64_10.dll'; dlerror: cusolver64_10.dll not found
2021-05-03 14:00:38.558695: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll
2021-05-03 14:00:38.559531: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cudnn64_8.dll'; dlerror: cudnn64_8.dll not found
2021-05-03 14:00:38.559626: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1757] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
2021-05-03 14:00:38.560184: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-05-03 14:00:38.561631: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-05-03 14:00:38.561729: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]
2021-05-03 14:00:38.561800: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
> # Compile Model
> model %>% compile(optimizer = optimizer_adam(lr=FLAGS$learning_rate),
+ loss = 'categorical_crossentropy',
+ .... [TRUNCATED]
> # Train Model
> model %>% fit(as.matrix(kdd_train_x_ohe), as.matrix(kdd_train_y_ohe),
+ batch_size=FLAGS$batch_size,
+ .... [TRUNCATED] 2021-05-03 14:00:38.762827: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)Epoch 1/100
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12505/12505 [==============================] - 9s 730us/step - loss: 651.3085 - accuracy: 0.9667 - val_loss: 5.3086 - val_accuracy: 0.9935
Epoch 2/100
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12505/12505 [==============================] - 8s 652us/step - loss: 47.8429 - accuracy: 0.9893
12505/12505 [==============================] - 9s 703us/step - loss: 47.8486 - accuracy: 0.9893 - val_loss: 13.3504 - val_accuracy: 0.9870
Epoch 3/100
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12505/12505 [==============================] - 9s 702us/step - loss: 9.8223 - accuracy: 0.9915 - val_loss: 17.4252 - val_accuracy: 0.9948
Epoch 4/100
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12505/12505 [==============================] - 8s 647us/step - loss: 35.7927 - accuracy: 0.9937
12505/12505 [==============================] - 9s 698us/step - loss: 35.7914 - accuracy: 0.9937 - val_loss: 7.4782 - val_accuracy: 0.9941
Epoch 5/100
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12505/12505 [==============================] - 9s 691us/step - loss: 232.4981 - accuracy: 0.9934 - val_loss: 7.4821 - val_accuracy: 0.9952
Epoch 6/100
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12505/12505 [==============================] - 9s 694us/step - loss: 84.4811 - accuracy: 0.9938 - val_loss: 5.1890 - val_accuracy: 0.9949
Epoch 7/100
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12505/12505 [==============================] - 8s 626us/step - loss: 2.3418 - accuracy: 0.9961
12505/12505 [==============================] - 8s 675us/step - loss: 2.3418 - accuracy: 0.9961 - val_loss: 0.0297 - val_accuracy: 0.9936
Epoch 8/100
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12505/12505 [==============================] - 8s 640us/step - loss: 0.0331 - accuracy: 0.9938
12505/12505 [==============================] - 9s 689us/step - loss: 0.0331 - accuracy: 0.9938 - val_loss: 0.0186 - val_accuracy: 0.9931
Epoch 9/100
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12505/12505 [==============================] - 8s 636us/step - loss: 0.1009 - accuracy: 0.9935
12505/12505 [==============================] - 9s 684us/step - loss: 0.1009 - accuracy: 0.9935 - val_loss: 0.0224 - val_accuracy: 0.9933
Epoch 10/100
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12505/12505 [==============================] - 8s 638us/step - loss: 0.0269 - accuracy: 0.9936
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Epoch 11/100
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12505/12505 [==============================] - 8s 642us/step - loss: 0.0333 - accuracy: 0.9939
12505/12505 [==============================] - 9s 691us/step - loss: 0.0333 - accuracy: 0.9939 - val_loss: 0.0210 - val_accuracy: 0.9939
Epoch 12/100
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12505/12505 [==============================] - 8s 632us/step - loss: 0.0311 - accuracy: 0.9937
12505/12505 [==============================] - 9s 682us/step - loss: 0.0311 - accuracy: 0.9937 - val_loss: 0.0197 - val_accuracy: 0.9915
Epoch 13/100
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11702/12505 [===========================>..] - ETA: 0s - loss: 0.0318 - accuracy: 0.9937
11738/12505 [===========================>..] - ETA: 0s - loss: 0.0318 - accuracy: 0.9937Warning in tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]), :
closing unused connection 15 (<-DESKTOP-9FJF8LP:11960)
Warning in tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]), :
closing unused connection 14 (<-DESKTOP-9FJF8LP:11960)
Warning in tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]), :
closing unused connection 13 (<-DESKTOP-9FJF8LP:11960)
Warning in tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]), :
closing unused connection 12 (<-DESKTOP-9FJF8LP:11960)
Warning in tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]), :
closing unused connection 11 (<-DESKTOP-9FJF8LP:11960)
Warning in tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]), :
closing unused connection 10 (<-DESKTOP-9FJF8LP:11960)
Warning in tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]), :
closing unused connection 9 (<-DESKTOP-9FJF8LP:11960)
Warning in tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]), :
closing unused connection 8 (<-DESKTOP-9FJF8LP:11960)
Warning in tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]), :
closing unused connection 7 (<-DESKTOP-9FJF8LP:11960)
Warning in tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]), :
closing unused connection 6 (<-DESKTOP-9FJF8LP:11960)
Warning in tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]), :
closing unused connection 5 (<-DESKTOP-9FJF8LP:11960)
Warning in tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]), :
closing unused connection 4 (<-DESKTOP-9FJF8LP:11960)
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12505/12505 [==============================] - 8s 633us/step - loss: 0.0319 - accuracy: 0.9938
12505/12505 [==============================] - 9s 686us/step - loss: 0.0319 - accuracy: 0.9938 - val_loss: 0.4520 - val_accuracy: 0.9940
Epoch 14/100
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12505/12505 [==============================] - 8s 671us/step - loss: 0.1411 - accuracy: 0.9939 - val_loss: 0.0247 - val_accuracy: 0.9937
Epoch 15/100
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12505/12505 [==============================] - 8s 647us/step - loss: 0.0641 - accuracy: 0.9938
12505/12505 [==============================] - 9s 697us/step - loss: 0.0641 - accuracy: 0.9938 - val_loss: 0.0137 - val_accuracy: 0.9943
Epoch 16/100
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12505/12505 [==============================] - 8s 625us/step - loss: 0.0502 - accuracy: 0.9942
12505/12505 [==============================] - 8s 675us/step - loss: 0.0502 - accuracy: 0.9942 - val_loss: 0.0135 - val_accuracy: 0.9940
Epoch 17/100
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12505/12505 [==============================] - 8s 644us/step - loss: 0.0205 - accuracy: 0.9940
12505/12505 [==============================] - 9s 694us/step - loss: 0.0205 - accuracy: 0.9940 - val_loss: 0.0227 - val_accuracy: 0.9943
Epoch 18/100
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Epoch 19/100
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12505/12505 [==============================] - 8s 645us/step - loss: 0.0286 - accuracy: 0.9942
12505/12505 [==============================] - 9s 696us/step - loss: 0.0286 - accuracy: 0.9942 - val_loss: 0.0219 - val_accuracy: 0.9942
Epoch 20/100
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12505/12505 [==============================] - 8s 618us/step - loss: 0.0187 - accuracy: 0.9943
12505/12505 [==============================] - 8s 668us/step - loss: 0.0187 - accuracy: 0.9943 - val_loss: 0.0189 - val_accuracy: 0.9938
Epoch 21/100
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12505/12505 [==============================] - 8s 631us/step - loss: 0.0195 - accuracy: 0.9942
12505/12505 [==============================] - 9s 681us/step - loss: 0.0195 - accuracy: 0.9942 - val_loss: 0.1561 - val_accuracy: 0.9950
Epoch 22/100
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12505/12505 [==============================] - 8s 620us/step - loss: 0.1701 - accuracy: 0.9939
12505/12505 [==============================] - 8s 671us/step - loss: 0.1701 - accuracy: 0.9939 - val_loss: 0.0138 - val_accuracy: 0.9939
Epoch 23/100
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12505/12505 [==============================] - 8s 641us/step - loss: 0.1094 - accuracy: 0.9940
12505/12505 [==============================] - 9s 691us/step - loss: 0.1094 - accuracy: 0.9940 - val_loss: 0.0163 - val_accuracy: 0.9939
Epoch 24/100
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12505/12505 [==============================] - 8s 647us/step - loss: 0.0517 - accuracy: 0.9938
12505/12505 [==============================] - 9s 698us/step - loss: 0.0517 - accuracy: 0.9938 - val_loss: 0.0260 - val_accuracy: 0.9939
Epoch 25/100
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12505/12505 [==============================] - 8s 660us/step - loss: 0.0620 - accuracy: 0.9952
12505/12505 [==============================] - 9s 710us/step - loss: 0.0620 - accuracy: 0.9952 - val_loss: 0.0296 - val_accuracy: 0.9936
Epoch 26/100
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Epoch 27/100
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12505/12505 [==============================] - 8s 625us/step - loss: 0.0863 - accuracy: 0.9947
12505/12505 [==============================] - 8s 675us/step - loss: 0.0863 - accuracy: 0.9947 - val_loss: 0.0139 - val_accuracy: 0.9940
Epoch 28/100
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12505/12505 [==============================] - 8s 644us/step - loss: 0.0289 - accuracy: 0.9947
12505/12505 [==============================] - 9s 693us/step - loss: 0.0289 - accuracy: 0.9947 - val_loss: 0.0135 - val_accuracy: 0.9947
Epoch 29/100
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Epoch 30/100
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12505/12505 [==============================] - 8s 651us/step - loss: 0.0165 - accuracy: 0.9947
12505/12505 [==============================] - 9s 702us/step - loss: 0.0165 - accuracy: 0.9947 - val_loss: 0.0147 - val_accuracy: 0.9963
Epoch 31/100
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12505/12505 [==============================] - 8s 661us/step - loss: 0.0315 - accuracy: 0.9942
12505/12505 [==============================] - 9s 712us/step - loss: 0.0315 - accuracy: 0.9942 - val_loss: 0.0148 - val_accuracy: 0.9940
Epoch 32/100
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12505/12505 [==============================] - 9s 710us/step - loss: 0.1218 - accuracy: 0.9954 - val_loss: 0.0263 - val_accuracy: 0.9942
Epoch 33/100
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12505/12505 [==============================] - 8s 653us/step - loss: 0.0250 - accuracy: 0.9953
12505/12505 [==============================] - 9s 704us/step - loss: 0.0250 - accuracy: 0.9953 - val_loss: 0.0138 - val_accuracy: 0.9948
Epoch 34/100
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Epoch 35/100
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12505/12505 [==============================] - 8s 669us/step - loss: 0.0469 - accuracy: 0.9950
12505/12505 [==============================] - 9s 719us/step - loss: 0.0469 - accuracy: 0.9950 - val_loss: 0.0138 - val_accuracy: 0.9937
Epoch 36/100
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12505/12505 [==============================] - 8s 630us/step - loss: 0.0136 - accuracy: 0.9953
12505/12505 [==============================] - 9s 680us/step - loss: 0.0136 - accuracy: 0.9953 - val_loss: 0.1017 - val_accuracy: 0.9977
Epoch 37/100
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Epoch 38/100
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12505/12505 [==============================] - 8s 675us/step - loss: 0.0157 - accuracy: 0.9945 - val_loss: 0.0102 - val_accuracy: 0.9971
Epoch 39/100
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12505/12505 [==============================] - 8s 639us/step - loss: 0.2743 - accuracy: 0.9942
12505/12505 [==============================] - 9s 689us/step - loss: 0.2743 - accuracy: 0.9942 - val_loss: 0.0087 - val_accuracy: 0.9986
Epoch 40/100
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12505/12505 [==============================] - 8s 638us/step - loss: 0.0562 - accuracy: 0.9978
12505/12505 [==============================] - 9s 688us/step - loss: 0.0562 - accuracy: 0.9978 - val_loss: 0.0325 - val_accuracy: 0.9837
Epoch 41/100
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12505/12505 [==============================] - 8s 621us/step - loss: 0.0303 - accuracy: 0.9933
12505/12505 [==============================] - 8s 671us/step - loss: 0.0303 - accuracy: 0.9933 - val_loss: 0.0104 - val_accuracy: 0.9980
Epoch 42/100
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Epoch 43/100
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12505/12505 [==============================] - 8s 630us/step - loss: 0.1159 - accuracy: 0.9959
12505/12505 [==============================] - 9s 680us/step - loss: 0.1160 - accuracy: 0.9959 - val_loss: 0.0093 - val_accuracy: 0.9978
Epoch 44/100
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12505/12505 [==============================] - 8s 641us/step - loss: 0.0423 - accuracy: 0.9980
12505/12505 [==============================] - 9s 691us/step - loss: 0.0423 - accuracy: 0.9980 - val_loss: 0.0072 - val_accuracy: 0.9982
Epoch 45/100
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Epoch 46/100
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12505/12505 [==============================] - 8s 645us/step - loss: 0.1101 - accuracy: 0.9970
12505/12505 [==============================] - 9s 695us/step - loss: 0.1101 - accuracy: 0.9970 - val_loss: 0.0154 - val_accuracy: 0.9938
Epoch 47/100
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12505/12505 [==============================] - 8s 623us/step - loss: 0.0221 - accuracy: 0.9953
12505/12505 [==============================] - 8s 673us/step - loss: 0.0221 - accuracy: 0.9953 - val_loss: 0.0170 - val_accuracy: 0.9943
Epoch 48/100
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12505/12505 [==============================] - 8s 645us/step - loss: 0.0676 - accuracy: 0.9939
12505/12505 [==============================] - 9s 695us/step - loss: 0.0676 - accuracy: 0.9939 - val_loss: 0.0274 - val_accuracy: 0.9988
Epoch 49/100
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12505/12505 [==============================] - 8s 637us/step - loss: 0.0376 - accuracy: 0.9950
12505/12505 [==============================] - 9s 691us/step - loss: 0.0376 - accuracy: 0.9950 - val_loss: 0.0268 - val_accuracy: 0.9952
Epoch 50/100
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Epoch 51/100
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12505/12505 [==============================] - 8s 621us/step - loss: 0.1110 - accuracy: 0.9940
12505/12505 [==============================] - 8s 671us/step - loss: 0.1110 - accuracy: 0.9940 - val_loss: 0.0146 - val_accuracy: 0.9942
Epoch 52/100
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12505/12505 [==============================] - 8s 626us/step - loss: 0.0190 - accuracy: 0.9943
12505/12505 [==============================] - 8s 675us/step - loss: 0.0190 - accuracy: 0.9943 - val_loss: 0.0131 - val_accuracy: 0.9940
Epoch 53/100
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12505/12505 [==============================] - 8s 641us/step - loss: 0.0357 - accuracy: 0.9944
12505/12505 [==============================] - 9s 691us/step - loss: 0.0357 - accuracy: 0.9944 - val_loss: 0.0125 - val_accuracy: 0.9972
Epoch 54/100
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12505/12505 [==============================] - 8s 620us/step - loss: 0.1251 - accuracy: 0.9933
12505/12505 [==============================] - 8s 671us/step - loss: 0.1251 - accuracy: 0.9933 - val_loss: 0.0151 - val_accuracy: 0.9940
Epoch 55/100
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12505/12505 [==============================] - 8s 623us/step - loss: 0.1119 - accuracy: 0.9956
12505/12505 [==============================] - 8s 674us/step - loss: 0.1119 - accuracy: 0.9956 - val_loss: 0.0080 - val_accuracy: 0.9982
Epoch 56/100
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12505/12505 [==============================] - 8s 623us/step - loss: 0.0178 - accuracy: 0.9981
12505/12505 [==============================] - 8s 674us/step - loss: 0.0178 - accuracy: 0.9981 - val_loss: 0.0151 - val_accuracy: 0.9967
Epoch 57/100
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12505/12505 [==============================] - 8s 624us/step - loss: 0.1454 - accuracy: 0.9973
12505/12505 [==============================] - 9s 681us/step - loss: 0.1454 - accuracy: 0.9973 - val_loss: 0.0281 - val_accuracy: 0.9921
Epoch 58/100
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12505/12505 [==============================] - 8s 625us/step - loss: 0.0118 - accuracy: 0.9960
12505/12505 [==============================] - 8s 676us/step - loss: 0.0118 - accuracy: 0.9960 - val_loss: 0.2922 - val_accuracy: 0.9681
Epoch 59/100
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12505/12505 [==============================] - 8s 648us/step - loss: 0.0415 - accuracy: 0.9916
12505/12505 [==============================] - 9s 700us/step - loss: 0.0415 - accuracy: 0.9916 - val_loss: 0.0107 - val_accuracy: 0.9982
Epoch 60/100
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12505/12505 [==============================] - 8s 637us/step - loss: 0.0521 - accuracy: 0.9960
12505/12505 [==============================] - 9s 691us/step - loss: 0.0521 - accuracy: 0.9960 - val_loss: 0.9109 - val_accuracy: 0.9987
Epoch 61/100
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12505/12505 [==============================] - 8s 666us/step - loss: 0.2552 - accuracy: 0.9951 - val_loss: 0.0116 - val_accuracy: 0.9976
Epoch 62/100
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12505/12505 [==============================] - 8s 616us/step - loss: 0.0109 - accuracy: 0.9975
12505/12505 [==============================] - 8s 666us/step - loss: 0.0109 - accuracy: 0.9975 - val_loss: 0.0357 - val_accuracy: 0.9981
Epoch 63/100
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12505/12505 [==============================] - 8s 638us/step - loss: 0.0141 - accuracy: 0.9956
12505/12505 [==============================] - 9s 688us/step - loss: 0.0141 - accuracy: 0.9956 - val_loss: 0.0770 - val_accuracy: 0.9983
Epoch 64/100
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12505/12505 [==============================] - 8s 635us/step - loss: 0.0391 - accuracy: 0.9971
12505/12505 [==============================] - 9s 685us/step - loss: 0.0391 - accuracy: 0.9971 - val_loss: 0.0163 - val_accuracy: 0.9944
Epoch 65/100
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12505/12505 [==============================] - 8s 661us/step - loss: 0.0293 - accuracy: 0.9941
12505/12505 [==============================] - 9s 712us/step - loss: 0.0293 - accuracy: 0.9941 - val_loss: 0.0148 - val_accuracy: 0.9944
Epoch 66/100
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12505/12505 [==============================] - 8s 640us/step - loss: 0.3323 - accuracy: 0.9943
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Epoch 67/100
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12505/12505 [==============================] - 8s 649us/step - loss: 0.0941 - accuracy: 0.9943
12505/12505 [==============================] - 9s 699us/step - loss: 0.0941 - accuracy: 0.9943 - val_loss: 0.0142 - val_accuracy: 0.9943
Epoch 68/100
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12505/12505 [==============================] - 8s 638us/step - loss: 0.0703 - accuracy: 0.9941
12505/12505 [==============================] - 9s 688us/step - loss: 0.0703 - accuracy: 0.9941 - val_loss: 0.0688 - val_accuracy: 0.9940
Epoch 69/100
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12505/12505 [==============================] - 8s 640us/step - loss: 0.0278 - accuracy: 0.9942
12505/12505 [==============================] - 9s 691us/step - loss: 0.0278 - accuracy: 0.9942 - val_loss: 0.0181 - val_accuracy: 0.9944
Epoch 70/100
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12505/12505 [==============================] - 8s 635us/step - loss: 0.0578 - accuracy: 0.9945
12505/12505 [==============================] - 9s 685us/step - loss: 0.0578 - accuracy: 0.9945 - val_loss: 0.0342 - val_accuracy: 0.9932
Epoch 71/100
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12505/12505 [==============================] - 8s 636us/step - loss: 0.0163 - accuracy: 0.9943
12505/12505 [==============================] - 9s 688us/step - loss: 0.0163 - accuracy: 0.9943 - val_loss: 0.0548 - val_accuracy: 0.9945
Epoch 72/100
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12505/12505 [==============================] - 8s 626us/step - loss: 0.0252 - accuracy: 0.9943
12505/12505 [==============================] - 8s 677us/step - loss: 0.0253 - accuracy: 0.9943 - val_loss: 0.0313 - val_accuracy: 0.9927
Epoch 73/100
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12505/12505 [==============================] - 8s 624us/step - loss: 0.0589 - accuracy: 0.9942
12505/12505 [==============================] - 8s 674us/step - loss: 0.0589 - accuracy: 0.9942 - val_loss: 0.0225 - val_accuracy: 0.9938
Epoch 74/100
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12505/12505 [==============================] - 8s 652us/step - loss: 0.0210 - accuracy: 0.9939
12505/12505 [==============================] - 9s 702us/step - loss: 0.0210 - accuracy: 0.9939 - val_loss: 0.0180 - val_accuracy: 0.9936
Epoch 75/100
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12505/12505 [==============================] - 9s 688us/step - loss: 1.9764 - accuracy: 0.9940 - val_loss: 0.0214 - val_accuracy: 0.9934
Epoch 76/100
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12505/12505 [==============================] - 8s 636us/step - loss: 0.0594 - accuracy: 0.9943
12505/12505 [==============================] - 9s 686us/step - loss: 0.0595 - accuracy: 0.9943 - val_loss: 0.6588 - val_accuracy: 0.9964
Epoch 77/100
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12505/12505 [==============================] - 9s 687us/step - loss: 1.2627 - accuracy: 0.9950 - val_loss: 0.4138 - val_accuracy: 0.9916
Epoch 78/100
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12505/12505 [==============================] - 8s 633us/step - loss: 0.0466 - accuracy: 0.9949
12505/12505 [==============================] - 9s 684us/step - loss: 0.0466 - accuracy: 0.9949 - val_loss: 0.0155 - val_accuracy: 0.9944
Epoch 79/100
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12505/12505 [==============================] - 8s 638us/step - loss: 0.0494 - accuracy: 0.9944
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Epoch 80/100
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12505/12505 [==============================] - 8s 624us/step - loss: 0.0215 - accuracy: 0.9944
12505/12505 [==============================] - 8s 674us/step - loss: 0.0215 - accuracy: 0.9944 - val_loss: 0.0204 - val_accuracy: 0.9943
Epoch 81/100
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12505/12505 [==============================] - 8s 638us/step - loss: 0.0155 - accuracy: 0.9946
12505/12505 [==============================] - 9s 688us/step - loss: 0.0155 - accuracy: 0.9946 - val_loss: 0.0763 - val_accuracy: 0.9941
Epoch 82/100
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Epoch 83/100
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12505/12505 [==============================] - 9s 694us/step - loss: 0.0226 - accuracy: 0.9945 - val_loss: 0.0113 - val_accuracy: 0.9952
Epoch 84/100
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12505/12505 [==============================] - 8s 655us/step - loss: 0.0476 - accuracy: 0.9948
12505/12505 [==============================] - 9s 706us/step - loss: 0.0476 - accuracy: 0.9948 - val_loss: 0.0287 - val_accuracy: 0.9955
Epoch 85/100
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12505/12505 [==============================] - 8s 661us/step - loss: 0.0137 - accuracy: 0.9946
12505/12505 [==============================] - 9s 710us/step - loss: 0.0137 - accuracy: 0.9946 - val_loss: 0.0214 - val_accuracy: 0.9945
Epoch 86/100
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12505/12505 [==============================] - 8s 639us/step - loss: 15.7699 - accuracy: 0.9947
12505/12505 [==============================] - 9s 690us/step - loss: 15.7697 - accuracy: 0.9947 - val_loss: 0.0563 - val_accuracy: 0.9942
Epoch 87/100
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12505/12505 [==============================] - 8s 616us/step - loss: 0.0152 - accuracy: 0.9947
12505/12505 [==============================] - 8s 666us/step - loss: 0.0152 - accuracy: 0.9947 - val_loss: 0.0224 - val_accuracy: 0.9939
Epoch 88/100
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12505/12505 [==============================] - 8s 626us/step - loss: 0.1368 - accuracy: 0.9943
12505/12505 [==============================] - 8s 679us/step - loss: 0.1368 - accuracy: 0.9943 - val_loss: 0.0259 - val_accuracy: 0.9941
Epoch 89/100
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12505/12505 [==============================] - 8s 626us/step - loss: 0.0230 - accuracy: 0.9946
12505/12505 [==============================] - 8s 676us/step - loss: 0.0230 - accuracy: 0.9946 - val_loss: 0.0141 - val_accuracy: 0.9944
Epoch 90/100
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Epoch 91/100
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12505/12505 [==============================] - 9s 696us/step - loss: 0.0145 - accuracy: 0.9951 - val_loss: 0.0176 - val_accuracy: 0.9943
Epoch 92/100
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12505/12505 [==============================] - 8s 628us/step - loss: 0.0162 - accuracy: 0.9946
12505/12505 [==============================] - 8s 676us/step - loss: 0.0162 - accuracy: 0.9946 - val_loss: 0.0356 - val_accuracy: 0.9944
Epoch 93/100
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12505/12505 [==============================] - 8s 635us/step - loss: 0.0138 - accuracy: 0.9945
12505/12505 [==============================] - 9s 687us/step - loss: 0.0138 - accuracy: 0.9945 - val_loss: 0.0212 - val_accuracy: 0.9944
Epoch 94/100
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12505/12505 [==============================] - 8s 628us/step - loss: 0.0312 - accuracy: 0.9945
12505/12505 [==============================] - 9s 681us/step - loss: 0.0312 - accuracy: 0.9945 - val_loss: 0.0145 - val_accuracy: 0.9942
Epoch 95/100
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12505/12505 [==============================] - 8s 617us/step - loss: 0.0666 - accuracy: 0.9947
12505/12505 [==============================] - 8s 668us/step - loss: 0.0666 - accuracy: 0.9947 - val_loss: 0.0157 - val_accuracy: 0.9944
Epoch 96/100
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12505/12505 [==============================] - 8s 619us/step - loss: 0.0111 - accuracy: 0.9953
12505/12505 [==============================] - 8s 670us/step - loss: 0.0111 - accuracy: 0.9953 - val_loss: 0.9958 - val_accuracy: 0.9959
Epoch 97/100
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12505/12505 [==============================] - 8s 637us/step - loss: 0.0479 - accuracy: 0.9947
12505/12505 [==============================] - 9s 687us/step - loss: 0.0479 - accuracy: 0.9947 - val_loss: 0.0166 - val_accuracy: 0.9945
Epoch 98/100
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12505/12505 [==============================] - 8s 620us/step - loss: 0.6200 - accuracy: 0.9948
12505/12505 [==============================] - 8s 670us/step - loss: 0.6200 - accuracy: 0.9948 - val_loss: 0.0657 - val_accuracy: 0.9964
Epoch 99/100
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12505/12505 [==============================] - 8s 619us/step - loss: 0.0133 - accuracy: 0.9963
12505/12505 [==============================] - 8s 669us/step - loss: 0.0133 - accuracy: 0.9963 - val_loss: 0.0207 - val_accuracy: 0.9927
Epoch 100/100
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Run completed: runs/2021-05-03T18-00-20ZstopCluster(cl)
runs $ run_dir : chr "runs/2021-05-03T18-00-20Z"
$ metric_loss : num 0.0285
$ metric_accuracy : num 0.995
$ metric_val_loss : num 0.0252
$ metric_val_accuracy: num 0.994
$ flag_nodes : int 32
$ flag_batch_size : int 32
$ flag_activation : chr "relu"
$ flag_learning_rate : num 0.001
$ flag_epochs : int 100
$ epochs : int 100
$ epochs_completed : int 100
$ metrics : chr "(metrics data frame)"
$ model : chr "(model summary)"
$ loss_function : chr "categorical_crossentropy"
$ optimizer : chr "<tensorflow.python.keras.optimizer_v2.adam.Adam>"
$ learning_rate : num 0.001
$ script : chr "kdd_hyper.R"
$ start : POSIXct[1:1], format: "2021-05-03 18:00:20"
$ end : POSIXct[1:1], format: "2021-05-03 18:15:11"
$ completed : logi TRUE
$ output : chr "(script ouptut)"
$ source_code : chr "(source archive)"
$ context : chr "local"
$ type : chr "training"view_run(runs$run_dir[which.max(runs$metric_val_accuracy)])argument 'compressed' is ignored for the internal methodincomplete final line found on 'C:\Users\cory\AppData\Local\Temp\Rtmp8WsDAi\file5c5c5ef74004/source/kdd_hyper.R'incomplete final line found on 'C:\Users\cory\AppData\Local\Temp\Rtmp8WsDAi\file5c5c5ef74004/source/min_final_error.R'# Model Evaluation
model <- keras_model_sequential() %>%
layer_dense(units=32, activation="relu", input_shape=dim(kdd_train_x_ohe)[2]) %>%
layer_dense(units=32, activation="relu") %>%
layer_dense(units=5, activation='softmax') # 5 category classifiers
model %>% compile(optimizer = optimizer_adam(lr=.001),
loss = 'categorical_crossentropy',
metrics = c('accuracy'))
model %>% fit(as.matrix(kdd_train_x_ohe), as.matrix(kdd_train_y_ohe),
batch_size=32,
epochs=100,
validation_data=list(as.matrix(kdd_val_x_ohe), as.matrix(kdd_val_y_ohe))
)Epoch 1/100
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12505/12505 [==============================] - 8s 594us/step - loss: 92.7714 - accuracy: 0.9676
12505/12505 [==============================] - 8s 650us/step - loss: 92.7883 - accuracy: 0.9676 - val_loss: 5.7064 - val_accuracy: 0.9939
Epoch 2/100
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12505/12505 [==============================] - 9s 685us/step - loss: 16.4284 - accuracy: 0.9901
12505/12505 [==============================] - 9s 736us/step - loss: 16.4302 - accuracy: 0.9901 - val_loss: 18.5147 - val_accuracy: 0.9825
Epoch 3/100
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12505/12505 [==============================] - 8s 614us/step - loss: 79.2813 - accuracy: 0.9889
12505/12505 [==============================] - 8s 666us/step - loss: 79.3120 - accuracy: 0.9889 - val_loss: 10.2829 - val_accuracy: 0.9873
Epoch 4/100
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12505/12505 [==============================] - 8s 604us/step - loss: 201.9041 - accuracy: 0.9899
12505/12505 [==============================] - 8s 655us/step - loss: 201.8974 - accuracy: 0.9899 - val_loss: 31.8150 - val_accuracy: 0.9878
Epoch 5/100
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12505/12505 [==============================] - 8s 600us/step - loss: 607.4664 - accuracy: 0.9912
12505/12505 [==============================] - 8s 651us/step - loss: 607.4358 - accuracy: 0.9912 - val_loss: 3.0454 - val_accuracy: 0.9963
Epoch 6/100
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Epoch 7/100
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12505/12505 [==============================] - 8s 652us/step - loss: 27.2307 - accuracy: 0.9946 - val_loss: 0.0575 - val_accuracy: 0.9924
Epoch 8/100
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12505/12505 [==============================] - 8s 609us/step - loss: 0.0767 - accuracy: 0.9921
12505/12505 [==============================] - 8s 659us/step - loss: 0.0767 - accuracy: 0.9921 - val_loss: 0.0384 - val_accuracy: 0.9922
Epoch 9/100
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12505/12505 [==============================] - 8s 604us/step - loss: 0.0797 - accuracy: 0.9927
12505/12505 [==============================] - 8s 655us/step - loss: 0.0797 - accuracy: 0.9927 - val_loss: 0.1052 - val_accuracy: 0.9930
Epoch 10/100
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12505/12505 [==============================] - 8s 610us/step - loss: 0.1909 - accuracy: 0.9924
12505/12505 [==============================] - 8s 661us/step - loss: 0.1909 - accuracy: 0.9924 - val_loss: 0.0311 - val_accuracy: 0.9930
Epoch 11/100
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12505/12505 [==============================] - 8s 610us/step - loss: 0.0588 - accuracy: 0.9925
12505/12505 [==============================] - 8s 660us/step - loss: 0.0588 - accuracy: 0.9925 - val_loss: 0.0703 - val_accuracy: 0.9925
Epoch 12/100
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12505/12505 [==============================] - 7s 598us/step - loss: 0.0876 - accuracy: 0.9919
12505/12505 [==============================] - 8s 649us/step - loss: 0.0876 - accuracy: 0.9919 - val_loss: 0.0285 - val_accuracy: 0.9935
Epoch 13/100
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12505/12505 [==============================] - 8s 607us/step - loss: 0.0930 - accuracy: 0.9928
12505/12505 [==============================] - 8s 659us/step - loss: 0.0930 - accuracy: 0.9928 - val_loss: 0.1286 - val_accuracy: 0.9930
Epoch 14/100
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12505/12505 [==============================] - 8s 600us/step - loss: 0.0881 - accuracy: 0.9927
12505/12505 [==============================] - 8s 653us/step - loss: 0.0881 - accuracy: 0.9927 - val_loss: 0.0241 - val_accuracy: 0.9927
Epoch 15/100
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12505/12505 [==============================] - 7s 597us/step - loss: 0.0297 - accuracy: 0.9930
12505/12505 [==============================] - 8s 646us/step - loss: 0.0297 - accuracy: 0.9930 - val_loss: 0.0263 - val_accuracy: 0.9921
Epoch 16/100
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12505/12505 [==============================] - 7s 600us/step - loss: 0.0352 - accuracy: 0.9930
12505/12505 [==============================] - 8s 650us/step - loss: 0.0352 - accuracy: 0.9930 - val_loss: 0.1703 - val_accuracy: 0.9925
Epoch 17/100
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Epoch 18/100
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12505/12505 [==============================] - 8s 603us/step - loss: 0.0418 - accuracy: 0.9931
12505/12505 [==============================] - 8s 654us/step - loss: 0.0418 - accuracy: 0.9931 - val_loss: 0.0491 - val_accuracy: 0.9938
Epoch 19/100
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12505/12505 [==============================] - 7s 598us/step - loss: 0.0617 - accuracy: 0.9932
12505/12505 [==============================] - 8s 649us/step - loss: 0.0617 - accuracy: 0.9932 - val_loss: 0.0226 - val_accuracy: 0.9934
Epoch 20/100
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12505/12505 [==============================] - 8s 657us/step - loss: 0.0935 - accuracy: 0.9932 - val_loss: 0.0281 - val_accuracy: 0.9935
Epoch 21/100
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12505/12505 [==============================] - 7s 596us/step - loss: 0.1185 - accuracy: 0.9935
12505/12505 [==============================] - 8s 645us/step - loss: 0.1185 - accuracy: 0.9935 - val_loss: 0.1622 - val_accuracy: 0.9953
Epoch 22/100
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12505/12505 [==============================] - 7s 594us/step - loss: 0.0944 - accuracy: 0.9931
12505/12505 [==============================] - 8s 644us/step - loss: 0.0944 - accuracy: 0.9931 - val_loss: 0.0164 - val_accuracy: 0.9936
Epoch 23/100
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12505/12505 [==============================] - 7s 595us/step - loss: 0.0648 - accuracy: 0.9935
12505/12505 [==============================] - 8s 645us/step - loss: 0.0648 - accuracy: 0.9935 - val_loss: 0.0139 - val_accuracy: 0.9944
Epoch 24/100
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12505/12505 [==============================] - 8s 600us/step - loss: 0.2300 - accuracy: 0.9939
12505/12505 [==============================] - 8s 650us/step - loss: 0.2300 - accuracy: 0.9939 - val_loss: 8.0824 - val_accuracy: 0.9947
Epoch 25/100
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12505/12505 [==============================] - 7s 595us/step - loss: 0.0273 - accuracy: 0.9946
12505/12505 [==============================] - 8s 647us/step - loss: 0.0273 - accuracy: 0.9946 - val_loss: 0.0184 - val_accuracy: 0.9936
Epoch 26/100
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12505/12505 [==============================] - 8s 600us/step - loss: 0.0345 - accuracy: 0.9940
12505/12505 [==============================] - 8s 651us/step - loss: 0.0345 - accuracy: 0.9940 - val_loss: 0.0235 - val_accuracy: 0.9934
Epoch 27/100
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12505/12505 [==============================] - 8s 652us/step - loss: 0.0242 - accuracy: 0.9935 - val_loss: 0.0650 - val_accuracy: 0.9936
Epoch 28/100
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12505/12505 [==============================] - 7s 591us/step - loss: 0.0604 - accuracy: 0.9942
12505/12505 [==============================] - 8s 641us/step - loss: 0.0604 - accuracy: 0.9942 - val_loss: 2.3454 - val_accuracy: 0.9933
Epoch 29/100
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12505/12505 [==============================] - 8s 609us/step - loss: 0.2998 - accuracy: 0.9977
12505/12505 [==============================] - 8s 659us/step - loss: 0.2998 - accuracy: 0.9977 - val_loss: 0.0099 - val_accuracy: 0.9984
Epoch 30/100
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12505/12505 [==============================] - 8s 602us/step - loss: 0.0584 - accuracy: 0.9971
12505/12505 [==============================] - 8s 652us/step - loss: 0.0584 - accuracy: 0.9971 - val_loss: 0.1919 - val_accuracy: 0.9966
Epoch 31/100
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12505/12505 [==============================] - 7s 598us/step - loss: 8.4072 - accuracy: 0.9961
12505/12505 [==============================] - 8s 646us/step - loss: 8.4073 - accuracy: 0.9961 - val_loss: 0.0274 - val_accuracy: 0.9933
Epoch 32/100
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12505/12505 [==============================] - 8s 638us/step - loss: 0.0949 - accuracy: 0.9933 - val_loss: 0.0128 - val_accuracy: 0.9984
Epoch 33/100
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12505/12505 [==============================] - 7s 591us/step - loss: 0.0223 - accuracy: 0.9961
12505/12505 [==============================] - 8s 640us/step - loss: 0.0223 - accuracy: 0.9961 - val_loss: 0.0219 - val_accuracy: 0.9937
Epoch 34/100
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12505/12505 [==============================] - 7s 588us/step - loss: 0.0332 - accuracy: 0.9947
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Epoch 35/100
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12505/12505 [==============================] - 7s 592us/step - loss: 1.2988 - accuracy: 0.9944
12505/12505 [==============================] - 8s 641us/step - loss: 1.2987 - accuracy: 0.9944 - val_loss: 0.0126 - val_accuracy: 0.9981
Epoch 36/100
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12505/12505 [==============================] - 7s 588us/step - loss: 0.0222 - accuracy: 0.9971
12505/12505 [==============================] - 8s 638us/step - loss: 0.0222 - accuracy: 0.9971 - val_loss: 0.0213 - val_accuracy: 0.9936
Epoch 37/100
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12505/12505 [==============================] - 8s 646us/step - loss: 0.1126 - accuracy: 0.9933 - val_loss: 0.0213 - val_accuracy: 0.9939
Epoch 38/100
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12505/12505 [==============================] - 7s 600us/step - loss: 0.1043 - accuracy: 0.9936
12505/12505 [==============================] - 8s 648us/step - loss: 0.1043 - accuracy: 0.9936 - val_loss: 0.0192 - val_accuracy: 0.9934
Epoch 39/100
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12505/12505 [==============================] - 7s 587us/step - loss: 0.3345 - accuracy: 0.9938
12505/12505 [==============================] - 8s 636us/step - loss: 0.3346 - accuracy: 0.9938 - val_loss: 0.0247 - val_accuracy: 0.9928
Epoch 40/100
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12505/12505 [==============================] - 7s 590us/step - loss: 0.0289 - accuracy: 0.9936
12505/12505 [==============================] - 8s 639us/step - loss: 0.0289 - accuracy: 0.9936 - val_loss: 0.0244 - val_accuracy: 0.9936
Epoch 41/100
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12505/12505 [==============================] - 7s 587us/step - loss: 0.0410 - accuracy: 0.9936
12505/12505 [==============================] - 8s 637us/step - loss: 0.0410 - accuracy: 0.9936 - val_loss: 0.0208 - val_accuracy: 0.9937
Epoch 42/100
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12505/12505 [==============================] - 7s 590us/step - loss: 0.0491 - accuracy: 0.9939
12505/12505 [==============================] - 8s 640us/step - loss: 0.0491 - accuracy: 0.9939 - val_loss: 2.6050 - val_accuracy: 0.9935
Epoch 43/100
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12505/12505 [==============================] - 8s 600us/step - loss: 0.3552 - accuracy: 0.9937
12505/12505 [==============================] - 8s 652us/step - loss: 0.3552 - accuracy: 0.9937 - val_loss: 0.0243 - val_accuracy: 0.9937
Epoch 44/100
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12505/12505 [==============================] - 8s 613us/step - loss: 0.0274 - accuracy: 0.9938
12505/12505 [==============================] - 8s 664us/step - loss: 0.0274 - accuracy: 0.9938 - val_loss: 0.0961 - val_accuracy: 0.9938
Epoch 45/100
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12505/12505 [==============================] - 8s 649us/step - loss: 0.6187 - accuracy: 0.9934 - val_loss: 0.0437 - val_accuracy: 0.9937
Epoch 46/100
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12505/12505 [==============================] - 7s 594us/step - loss: 0.0574 - accuracy: 0.9936
12505/12505 [==============================] - 8s 645us/step - loss: 0.0574 - accuracy: 0.9936 - val_loss: 0.6418 - val_accuracy: 0.9935
Epoch 47/100
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12505/12505 [==============================] - 8s 601us/step - loss: 0.0780 - accuracy: 0.9936
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Epoch 48/100
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12505/12505 [==============================] - 7s 597us/step - loss: 0.0302 - accuracy: 0.9936
12505/12505 [==============================] - 8s 646us/step - loss: 0.0302 - accuracy: 0.9936 - val_loss: 0.0296 - val_accuracy: 0.9920
Epoch 49/100
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12505/12505 [==============================] - 7s 591us/step - loss: 0.0667 - accuracy: 0.9933
12505/12505 [==============================] - 8s 641us/step - loss: 0.0667 - accuracy: 0.9933 - val_loss: 0.0209 - val_accuracy: 0.9934
Epoch 50/100
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12505/12505 [==============================] - 7s 592us/step - loss: 0.0407 - accuracy: 0.9932
12505/12505 [==============================] - 8s 642us/step - loss: 0.0407 - accuracy: 0.9932 - val_loss: 0.0297 - val_accuracy: 0.9937
Epoch 51/100
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12505/12505 [==============================] - 7s 589us/step - loss: 0.0241 - accuracy: 0.9936
12505/12505 [==============================] - 8s 641us/step - loss: 0.0241 - accuracy: 0.9936 - val_loss: 0.0405 - val_accuracy: 0.9936
Epoch 52/100
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12505/12505 [==============================] - 7s 590us/step - loss: 0.0273 - accuracy: 0.9937
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Epoch 53/100
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12505/12505 [==============================] - 7s 595us/step - loss: 0.0226 - accuracy: 0.9934
12505/12505 [==============================] - 8s 645us/step - loss: 0.0226 - accuracy: 0.9934 - val_loss: 0.0654 - val_accuracy: 0.9935
Epoch 54/100
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12505/12505 [==============================] - 7s 598us/step - loss: 0.6710 - accuracy: 0.9935
12505/12505 [==============================] - 8s 648us/step - loss: 0.6710 - accuracy: 0.9935 - val_loss: 0.0695 - val_accuracy: 0.9935
Epoch 55/100
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12505/12505 [==============================] - 8s 641us/step - loss: 0.0428 - accuracy: 0.9936 - val_loss: 0.0146 - val_accuracy: 0.9936
Epoch 56/100
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12505/12505 [==============================] - 8s 608us/step - loss: 0.0212 - accuracy: 0.9937
12505/12505 [==============================] - 8s 658us/step - loss: 0.0212 - accuracy: 0.9937 - val_loss: 0.0288 - val_accuracy: 0.9936
Epoch 57/100
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12505/12505 [==============================] - 7s 591us/step - loss: 0.6738 - accuracy: 0.9937
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Epoch 58/100
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12505/12505 [==============================] - 8s 641us/step - loss: 0.0311 - accuracy: 0.9936 - val_loss: 0.0213 - val_accuracy: 0.9938
Epoch 59/100
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12505/12505 [==============================] - 7s 596us/step - loss: 0.0216 - accuracy: 0.9937
12505/12505 [==============================] - 8s 647us/step - loss: 0.0216 - accuracy: 0.9937 - val_loss: 0.0200 - val_accuracy: 0.9923
Epoch 60/100
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Epoch 61/100
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12505/12505 [==============================] - 7s 594us/step - loss: 0.0246 - accuracy: 0.9935
12505/12505 [==============================] - 8s 644us/step - loss: 0.0246 - accuracy: 0.9935 - val_loss: 0.0459 - val_accuracy: 0.9934
Epoch 62/100
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12505/12505 [==============================] - 7s 599us/step - loss: 0.0232 - accuracy: 0.9934
12505/12505 [==============================] - 8s 648us/step - loss: 0.0232 - accuracy: 0.9934 - val_loss: 0.0531 - val_accuracy: 0.9936
Epoch 63/100
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12505/12505 [==============================] - 8s 642us/step - loss: 0.0257 - accuracy: 0.9939 - val_loss: 0.0406 - val_accuracy: 0.9886
Epoch 64/100
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12505/12505 [==============================] - 7s 596us/step - loss: 0.0198 - accuracy: 0.9943
12505/12505 [==============================] - 8s 646us/step - loss: 0.0198 - accuracy: 0.9943 - val_loss: 0.0479 - val_accuracy: 0.9938
Epoch 65/100
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Epoch 66/100
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12505/12505 [==============================] - 8s 647us/step - loss: 0.0296 - accuracy: 0.9935 - val_loss: 0.0592 - val_accuracy: 0.9937
Epoch 67/100
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12505/12505 [==============================] - 7s 595us/step - loss: 0.0291 - accuracy: 0.9936
12505/12505 [==============================] - 8s 645us/step - loss: 0.0291 - accuracy: 0.9936 - val_loss: 0.0206 - val_accuracy: 0.9938
Epoch 68/100
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12505/12505 [==============================] - 7s 586us/step - loss: 0.0172 - accuracy: 0.9936
12505/12505 [==============================] - 8s 636us/step - loss: 0.0172 - accuracy: 0.9936 - val_loss: 0.2807 - val_accuracy: 0.9936
Epoch 69/100
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12505/12505 [==============================] - 7s 591us/step - loss: 0.0545 - accuracy: 0.9937
12505/12505 [==============================] - 8s 641us/step - loss: 0.0545 - accuracy: 0.9937 - val_loss: 0.0478 - val_accuracy: 0.9937
Epoch 70/100
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Epoch 71/100
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12505/12505 [==============================] - 8s 641us/step - loss: 0.0257 - accuracy: 0.9935 - val_loss: 0.0258 - val_accuracy: 0.9938
Epoch 72/100
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12505/12505 [==============================] - 7s 593us/step - loss: 0.0181 - accuracy: 0.9934
12505/12505 [==============================] - 8s 642us/step - loss: 0.0181 - accuracy: 0.9934 - val_loss: 0.0487 - val_accuracy: 0.9937
Epoch 73/100
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12505/12505 [==============================] - 7s 598us/step - loss: 0.9954 - accuracy: 0.9937
12505/12505 [==============================] - 8s 648us/step - loss: 0.9954 - accuracy: 0.9937 - val_loss: 0.0618 - val_accuracy: 0.9937
Epoch 74/100
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12505/12505 [==============================] - 7s 590us/step - loss: 0.0180 - accuracy: 0.9938
12505/12505 [==============================] - 8s 640us/step - loss: 0.0180 - accuracy: 0.9938 - val_loss: 0.0250 - val_accuracy: 0.9938
Epoch 75/100
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Epoch 76/100
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12505/12505 [==============================] - 8s 642us/step - loss: 0.0424 - accuracy: 0.9938 - val_loss: 0.0335 - val_accuracy: 0.9939
Epoch 77/100
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12505/12505 [==============================] - 7s 599us/step - loss: 0.0415 - accuracy: 0.9935
12505/12505 [==============================] - 8s 650us/step - loss: 0.0415 - accuracy: 0.9935 - val_loss: 0.0655 - val_accuracy: 0.9935
Epoch 78/100
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Epoch 79/100
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12505/12505 [==============================] - 7s 593us/step - loss: 0.0205 - accuracy: 0.9938
12505/12505 [==============================] - 8s 642us/step - loss: 0.0205 - accuracy: 0.9938 - val_loss: 0.0726 - val_accuracy: 0.9937
Epoch 80/100
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Epoch 81/100
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12505/12505 [==============================] - 7s 594us/step - loss: 0.0338 - accuracy: 0.9939
12505/12505 [==============================] - 8s 643us/step - loss: 0.0338 - accuracy: 0.9939 - val_loss: 0.0486 - val_accuracy: 0.9938
Epoch 82/100
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12505/12505 [==============================] - 7s 592us/step - loss: 0.0199 - accuracy: 0.9936
12505/12505 [==============================] - 8s 642us/step - loss: 0.0199 - accuracy: 0.9936 - val_loss: 0.0979 - val_accuracy: 0.9934
Epoch 83/100
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12505/12505 [==============================] - 8s 648us/step - loss: 0.0388 - accuracy: 0.9937 - val_loss: 0.0663 - val_accuracy: 0.9937
Epoch 84/100
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12505/12505 [==============================] - 7s 587us/step - loss: 0.0926 - accuracy: 0.9932
12505/12505 [==============================] - 8s 637us/step - loss: 0.0926 - accuracy: 0.9932 - val_loss: 0.0662 - val_accuracy: 0.9928
Epoch 85/100
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12505/12505 [==============================] - 7s 594us/step - loss: 0.0304 - accuracy: 0.9934
12505/12505 [==============================] - 8s 644us/step - loss: 0.0304 - accuracy: 0.9934 - val_loss: 0.0236 - val_accuracy: 0.9935
Epoch 86/100
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12505/12505 [==============================] - 7s 593us/step - loss: 0.0197 - accuracy: 0.9935
12505/12505 [==============================] - 8s 643us/step - loss: 0.0197 - accuracy: 0.9935 - val_loss: 0.0212 - val_accuracy: 0.9937
Epoch 87/100
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12505/12505 [==============================] - 7s 589us/step - loss: 3.8742 - accuracy: 0.9937
12505/12505 [==============================] - 8s 639us/step - loss: 3.8743 - accuracy: 0.9937 - val_loss: 0.0828 - val_accuracy: 0.9935
Epoch 88/100
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12505/12505 [==============================] - 8s 645us/step - loss: 2.7001 - accuracy: 0.9937 - val_loss: 0.1466 - val_accuracy: 0.9936
Epoch 89/100
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12505/12505 [==============================] - 7s 592us/step - loss: 0.0336 - accuracy: 0.9938
12505/12505 [==============================] - 8s 641us/step - loss: 0.0336 - accuracy: 0.9938 - val_loss: 0.1410 - val_accuracy: 0.9938
Epoch 90/100
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12505/12505 [==============================] - 7s 593us/step - loss: 0.0533 - accuracy: 0.9938
12505/12505 [==============================] - 8s 642us/step - loss: 0.0533 - accuracy: 0.9938 - val_loss: 0.1634 - val_accuracy: 0.9937
Epoch 91/100
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12505/12505 [==============================] - 8s 610us/step - loss: 0.0382 - accuracy: 0.9938
12505/12505 [==============================] - 8s 661us/step - loss: 0.0382 - accuracy: 0.9938 - val_loss: 0.1130 - val_accuracy: 0.9936
Epoch 92/100
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12505/12505 [==============================] - 7s 592us/step - loss: 0.0169 - accuracy: 0.9939
12505/12505 [==============================] - 8s 642us/step - loss: 0.0169 - accuracy: 0.9939 - val_loss: 0.1013 - val_accuracy: 0.9939
Epoch 93/100
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12505/12505 [==============================] - 8s 644us/step - loss: 0.0235 - accuracy: 0.9938 - val_loss: 0.0571 - val_accuracy: 0.9937
Epoch 94/100
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12505/12505 [==============================] - 7s 587us/step - loss: 0.8959 - accuracy: 0.9934
12505/12505 [==============================] - 8s 637us/step - loss: 0.8963 - accuracy: 0.9934 - val_loss: 0.0744 - val_accuracy: 0.9936
Epoch 95/100
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12505/12505 [==============================] - 7s 590us/step - loss: 0.1107 - accuracy: 0.9922
12505/12505 [==============================] - 8s 639us/step - loss: 0.1107 - accuracy: 0.9922 - val_loss: 0.0950 - val_accuracy: 0.9930
Epoch 96/100
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12505/12505 [==============================] - 7s 598us/step - loss: 0.0321 - accuracy: 0.9929
12505/12505 [==============================] - 8s 647us/step - loss: 0.0321 - accuracy: 0.9929 - val_loss: 0.0970 - val_accuracy: 0.9936
Epoch 97/100
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12505/12505 [==============================] - 7s 587us/step - loss: 0.0180 - accuracy: 0.9938
12505/12505 [==============================] - 8s 636us/step - loss: 0.0180 - accuracy: 0.9938 - val_loss: 0.0463 - val_accuracy: 0.9937
Epoch 98/100
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12505/12505 [==============================] - 7s 595us/step - loss: 0.3122 - accuracy: 0.9938
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Epoch 99/100
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Epoch 100/100
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12505/12505 [==============================] - 7s 597us/step - loss: 0.0311 - accuracy: 0.9935
12505/12505 [==============================] - 8s 648us/step - loss: 0.0311 - accuracy: 0.9935 - val_loss: 0.0340 - val_accuracy: 0.9926model %>% evaluate(as.matrix(kdd_test_x_ohe), as.matrix(kdd_test_y_ohe))
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1544/1544 [==============================] - 1s 384us/step - loss: 0.0275 - accuracy: 0.9928
1544/1544 [==============================] - 1s 384us/step - loss: 0.0275 - accuracy: 0.9928
loss accuracy
0.02748905 0.99283385 predictions = model %>% predict(as.matrix(kdd_test_x_ohe))
# Convert column names and object class to evaluate performance
colnames(predictions) = colnames(kdd_test_y_ohe)
predictions = as.data.frame(predictions)
kdd_test_y_ohe = as.data.frame(kdd_test_y_ohe)
predictions = predictions %>% rename("dos" = "eventCategory_dos",
"normal" = "eventCategory_normal",
"probe" = "eventCategory_probe",
"r21" = "eventCategory_r21",
"u2r" = "eventCategory_u2r")
kdd_test_y_ohe = kdd_test_y_ohe %>% rename("dos" = "eventCategory_dos",
"normal" = "eventCategory_normal",
"probe" = "eventCategory_probe",
"r21" = "eventCategory_r21",
"u2r" = "eventCategory_u2r")
# Generate CrossTable and AUC
w <- which(predictions==apply(predictions[,], 1, max), arr.ind = TRUE)
predictions_ct <- toupper(names(round(predictions))[w[order(w[,1]),2]])
w2 <- which(kdd_test_y_ohe==apply(kdd_test_y_ohe[,], 1, max), arr.ind = TRUE)
kdd_test_y_ct <- toupper(names(kdd_test_y_ohe)[w2[order(w2[,1]),2]])
CrossTable(predictions_ct, kdd_test_y_ct, prop.chisq=FALSE, prop.t=FALSE,
dnn=c("predicted","actual"))
Cell Contents
|-------------------------|
| N |
| N / Row Total |
| N / Col Total |
|-------------------------|
Total Observations in Table: 49399
| actual
predicted | DOS | NORMAL | PROBE | R21 | U2R | Row Total |
-------------|-----------|-----------|-----------|-----------|-----------|-----------|
DOS | 38886 | 1 | 1 | 1 | 0 | 38889 |
| 1.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.787 |
| 0.993 | 0.000 | 0.002 | 0.009 | 0.000 | |
-------------|-----------|-----------|-----------|-----------|-----------|-----------|
NORMAL | 256 | 9702 | 4 | 59 | 5 | 10026 |
| 0.026 | 0.968 | 0.000 | 0.006 | 0.000 | 0.203 |
| 0.007 | 0.997 | 0.010 | 0.527 | 1.000 | |
-------------|-----------|-----------|-----------|-----------|-----------|-----------|
PROBE | 3 | 23 | 405 | 0 | 0 | 431 |
| 0.007 | 0.053 | 0.940 | 0.000 | 0.000 | 0.009 |
| 0.000 | 0.002 | 0.988 | 0.000 | 0.000 | |
-------------|-----------|-----------|-----------|-----------|-----------|-----------|
R21 | 0 | 1 | 0 | 52 | 0 | 53 |
| 0.000 | 0.019 | 0.000 | 0.981 | 0.000 | 0.001 |
| 0.000 | 0.000 | 0.000 | 0.464 | 0.000 | |
-------------|-----------|-----------|-----------|-----------|-----------|-----------|
Column Total | 39145 | 9727 | 410 | 112 | 5 | 49399 |
| 0.792 | 0.197 | 0.008 | 0.002 | 0.000 | |
-------------|-----------|-----------|-----------|-----------|-----------|-----------|
auc(multiclass.roc(kdd_test_y$eventCategory, predictions))Multi-class area under the curve: 0.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